Information Collecting and Decision Making Via Tiered Information Network Systems

ABSTRACT

Techniques, apparatus and systems for information collecting and decision making based on networks of sensors and communication nodes for security monitoring and warning, disaster warning, counter-terrorism, and other applications associated with information collecting and decision making.

PRIORITY CLAIM AND RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 60/879,949 entitled “Security Network” and filed on Jan.10, 2007, the entire disclosure of which is incorporated by reference aspart of the specification of this application.

BACKGROUND

This application relates to information collecting and decision makingbased on networks of sensors and communication nodes and applications insecurity monitoring, security warning, counter-terrorism, and otherapplications associated with information collecting and decision making.

Various detectors or sensors can be used to obtain information fromtargets and such data collection from targets can be automated by usingcomputers and computer networks to store data from the detectors orsensors and to analyze the data from the detectors and sensors to makedecisions. Examples of such automated data collection, processing andanalysis systems, among others, include computer-based warning systemsfor generating early or timely warnings of natural and man-madedisasters or hazardous events in various applications including securityand counter-terrorism applications.

Some warning systems may require human intervention to either performthe threat analysis or to recognize when a significant threat ispresented and to initiate the warning. For example, the tsunami earlywarning system is one such system. In that system, seismic informationis collected and sent to a hub for processing. The information isanalyzed to determine the likelihood of tsunami generation. If there isa high probability of tsunami generation, a warning is issued to local,state, national and international users as well as the media. Theseusers, in turn, disseminate the tsunami information to the public,generally over commercial radio and television channels. Next, sea leveldata is gathered and analyzed to determine the presence of an actualtsunami. If a tsunami is detected, the warning area may be enlarged.Such a system relies upon central data analysis and human interventionfor providing the warning as a result, the processing of large amountsof data is limited because its reliance on the central hub and humananalysis.

In another example, a tornado warning system combines data from varioussources and human decision making to trigger outdoor sirens. The systempartners the National Weather Service, local emergency responseagencies, and major industries to provide the necessary communicationand coordination. A major source of data in the system is a network oftrained volunteer storm spotters and Ham Radio operators. These groupsall work together to make up an integrated system of hazard detection,consequence prediction, and warning dissemination. National WeatherService meteorologists use information from weather radar as well as thenetwork of trained spotters to issue severe weather warnings.

Attempts have been made to increase automation of such warning systemsby using computer processing and communication networks. Examples ofsuch systems are described in U.S. Pat. No. 6,169,476 entitled “earlywarning system for natural and manmade disasters” and issued to Flanaganand U.S. Pat. No. 6,930,596 entitled “system for detection of hazardousevents” and issued to Kulesz et al.

SUMMARY

This application describes, among others, techniques, apparatus andsystems for information collecting and decision making based on networksof sensors and communication nodes for security monitoring and warning,disaster warning, counter-terrorism, and other applications associatedwith information collecting and decision making.

In one aspect, a network system for collecting information from sensorsdescribed in this document includes a plurality of different tiernetworks in communication with one another. Each tier network comprisesa plurality of network nodes one of which is configured as a commandcenter of the tier network to collect data from other network nodeswithin the tier network and to direct the collected data to a centernode of a superordinate tier network. The different tier networks areconfigured to perform different and tier-specific data collection anddata processing tasks and at least one tier network being located withina superordinate tier network. This system includes a plurality ofsensors spatially distributed in the different tier networks to performsensing measurements, each sensor in communication with a respectivenetwork node to direct data of sensing measurements to the respectivenetwork node.

In another aspect, a network system for collecting information fromsensors described in this document includes a plurality of sensorsspatially distributed in a region of interest to perform sensingmeasurements; a plurality of sensor communication nodes, each sensorcommunication node in communication with at least one of the sensors toreceive data from the at least one sensor; communication links that linkthe sensor communication nodes into a plurality of tier networks ofsensor communication nodes based on geographic location attributes andnon-geographic location attributes, the plurality of tier networks beingconfigured to perform different data collection and data processingtasks based on the tier network attributes; and a processing mechanismto distribute data processing to the plurality of tier networks and toproduce a response based on the distributed data processing.

In another aspect, a method for using a security network described inthis document includes embedding threat detection software in aplurality of roadway traffic signal controllers; coupling at least onethreat detection sensor to a corresponding roadway traffic signalcontroller; and monitoring output of the at least one threat detectionsensor by the corresponding roadway traffic signal controller.

In yet another aspect, a security network described in this documentincludes a plurality of second tier networks, a plurality of third tiernetworks; and a plurality of fourth tier networks. At least one of thesecond tier networks comprises a plurality of fourth tier networks, andeach network is assigned tasks corresponding to the tier.

These and other implementations and their variations are described indetail in the attached drawings, the detailed description and theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of an example of a multi-tier security network.

FIG. 2 is a schematic of the security network in one implementation of amulti-tier configuration.

FIG. 3 is a schematic of a portion of the security network.

FIG. 4 is a schematic of a portion of an embodiment of the securitynetwork.

FIG. 5 is a schematic of another portion of an embodiment of thesecurity network.

FIG. 6 is a schematic of another portion of an embodiment of thesecurity network.

FIGS. 7A and 7B shows example designs of a sensor node.

FIG. 8 is a schematic of a traffic intersection incorporated in anembodiment of the security network of the present invention.

FIG. 9 shows an example of a multi-tier system in connection withvarious external computers and databases.

FIG. 10 shows an exemplary implementation of a multi-tier system wherethe sensor nodes include intersection nodes at different intersectionsof roadways.

FIG. 11 is a schematic of the architecture of an intersection node of anembodiment of the security network.

FIG. 12 is a schematic of the architecture of intermediate tier node ofan embodiment of the security network.

FIG. 13 is a schematic of the architecture of a high tier node of anembodiment of the security network.

FIG. 14 is an illustration of an embodiment of a portion of a visualdisplay provided in a user interface for a security network.

FIG. 15 is an illustration of an embodiment of another portion of avisual display provided in a user interface for a security network.

FIGS. 16-19 illustrate examples of data flows in the present system.

FIGS. 20 and 21 show examples of users in the present system.

FIGS. 22-25 are schematics illustrating a scenario in which a threat isdetected and tracked by a security network.

FIG. 26 is a schematic of a process of data collection that may beutilized by a security network.

DETAILED DESCRIPTION

Examples and implementations of techniques, apparatus and systems forinformation collecting and decision making based on networks of sensorsand communication nodes in this document can be used in variousapplications including security monitoring and warning, disasterwarning, counter-terrorism, and other applications associated withinformation collecting and decision making. Specific examples ofmulti-tier sub-network structures for data collection, processing andanalysis and tier-based task allocation are described below in thecontext of security networks and various technical features in thesespecific examples can be applied to a wide range applications related todata collection and decision making beyond security networks.

The security network examples described below can be used to providenational situation awareness, including threat detection and eventforecasting, assessment and response. Such a security network is anetwork of distributed multi-tier sub-networks. The network is definedeither comprehensively or in terms of its individual components andtheir integration. The network components may provide the ability tointegrate and apply decision logic as a function of data received byeither individual, combined, agnostic or specifically tailored sensordevices or systems. Additionally, the network components provide theability to cross correlate this information to a database and/or betweendatabases of pertinent information relative to the suspected threat inorder to verify and broaden the understanding of its scope. Componentsof the system may include notification and strategic response protocols,both generic and idiosyncratic to the protected area. Additionally,components of the system can include interfaces for integration withexisting sensors, sensor networks, data fusion systems and analyticsmodules. This integration with existing infrastructure providesadditional situational awareness by utilizing existing assets.

The sub-networks are organized on tiers in which each tier is assignedprocessing tasks specific for the tier. The distribution of tier-basedtasks allows for data overlap, cross-pollination, comparison, andcertainty testing of collected information and performed analyses. Thesub-networks are also capable of peer-to-peer communication regardlessof the tier hierarchy and data aggregation that allows the system todraw determinant conclusions. Although the network is primarily based onthe tier-based task distribution, in certain embodiments, the taskdistribution also correlates to a geographically organized tierstructure.

In one implementation, the security network forms a first tier andincludes a plurality of second tier networks, a plurality of third tiernetworks, and a plurality of fourth tier networks. This tiered structureprovides a subordinate-superordinate relationship of different tierswhere the fourth tier network is the most subordinate network and thefirst tier network is the most superordinate network. The scope of thefunctions of a subordinate network can be partially or completelyencompassed by the scope of functions of a superordinate network. Eachtier network includes network nodes one of which is configured as acommand center of the tier network to collect data from other networknodes within the tier network and to direct the collected data to acenter node of a superordinate tier network. The different tier networksare configured to perform different and tier-specific data collectionand data processing tasks. In one implementation, at least one tiernetwork is located within a superordinate tier network where networknodes of the tier network are network nodes of the superordinate tiernetwork. Two different tier networks may share one or more commonnetwork nodes while other network nodes in the two networks aredifferent.

In some applications including security networks, the tier structure maybe organized by geographic locations. The second tier networks may beregional networks. The third tier networks may be corridor networkscorresponding to interstate thoroughfares or other geographic regions.The fourth tier networks may be local networks, such as a towns,downtowns, neighborhoods, or specific target locations such asreservoirs and sports stadiums. Components of networks may overlap, forexample, a component of a regional network may also be a component of acorridor network so that communication handoff is simplified. Sensorscan be provided in the networks to collect various data to be stored inthe security network for data processing, data analysis and decisionmaking. In a sub network covering roadways, road traffic controllers canbe used to provide processing for the stations that form thesub-networks. In other implementations, sensors such as cameras, cargodetectors and other sensing devices may be used.

An example for providing a security network is provided that utilizesroad traffic controllers as one component. The method includes embeddingthreat detection software in a network of roadway traffic controllers.At least one threat detection sensor, such as chemical, biological,radiological, nuclear and/or explosive detection sensors, is coupled toa corresponding roadway traffic controller. The roadway trafficcontroller may be configured to gather data received from the sensor.The controllers are coupled to a command center that utilizes the datafrom the controllers to provide analysis, displays, predictions andpreemptive plans for monitoring and responding to a detected threat. Incertain embodiments, the roadway traffic controllers are configured toprovide low-level analysis of the data gathered.

FIG. 1 is a schematic of an example of a multi-tier security network.The security network 10 is comprised of a system of systems thatinteract at different tier levels to collect, process and analyze data.The system 10 is configured to provide collaborative analysis andcommunication capability between smaller groups of stations, or nodes.In particular, the network 10 can be configured to perform complexreasoning on data gathered across a highly distributed multi-modalsensor network based on a subsumption architecture. A subsumptionarchitecture is a way of decomposing complicated intelligent behaviorinto many “simple” behavior modules, which are in turn organized intolayers. Each layer implements a particular goal of an intelligent agent,and higher layers are increasingly more abstract. Each layer's goalsubsumes that of the underlying layers. This way, the lowest layers canwork like fast-adapting mechanisms (reflexes), while the higher layerscontrol the main direction to be taken in order to achieve the overallgoal. Feedback is given mainly through the environment. In theillustrated example, the security network 10 includes a plurality ofindependent and autonomous sub-networks 12, 14, and 16 of nodes that areassigned a plurality of scopes and perform tier-based processing andanalysis tasks.

In FIG. 1, the analyses performed at each tier can use episodic memorylogic so that the processing requirements may be broken down intotier-based tasks and distributed throughout the network. The system-wideepisodic memory is the selective retention of temporal, spatial, andcontextual data and facts that the system ingests from sensors orderives with respect to an event. The episodic memory in the example inFIG. 1 can be distributed hierarchically throughout the system and beinstantiated by criteria and value quantified logic at variant tierlayers within the system. The stored episodic data and meta-data can bepropagated or queried by computational tiers within the system tosupport reasoning processes. That distribution results in theperformance of temporal, spatial and collaborative reasoning andanalytics throughout the network hierarchy and provides a more evenlydistributed processing load as well as situational awareness breadth.

For example, the network 10 can be configured with various sensors toprovide identification of weapons of mass destruction, known terroristsand threat behavior patterns while providing a large-scale situationawareness that allows communication of a national common operationalpicture for critical incident response. The network also coordinates anddistributes a national response plan to first responders.

The network 10 can be configured to be context driven so that use ofsensors is prioritized and communication bandwidth is designated forparticular functions upon threat detection. The system's reliance onepisodic memory and simulation templates allows easy continualadaptation of the system through additional theoretical scenarios andallows lessons learned from actual experiences to be added back into thesystem as additional simulation templates. Additionally oralternatively, any intelligent programming paradigm may be incorporatedalone, in combination or hybridized, such as genetic algorithms, neuralnetworks, and/or fuzzy logic.

In an exemplary embodiment, the sub-networks are assigned various scopesthat correspond to data gathering, processing and analysis andincidentally correlate to a geographical scope. The network 10 includesgroups of local, metropolitan and regional stations, or nodes, as wellas at least one national station, or node. For example, the system mayinclude sub-groups, or sub-networks, of nodes that correspond to localroadway networks, regional networks, corridor networks and all of thosemay be combined to form a top-tiered national network. Security network10 relies upon tier-based task allocation to distribute processing loadand to reduce network traffic. The network capacity and speed areincreased by utilizing the distributed processing capabilitiesdistributed throughout the sub-networks.

Security network 10 is a first tier network that is constructed from aplurality of sub-networks, e.g., second tier networks 12, third tiernetworks 14 and fourth tier networks 16. Each of the sub-networks isconfigured to operate independently and autonomously from others of thesame or lower tier networks. It should be appreciated that the number oftiers and sub-networks in each tier is not limited and may be physicallyorganized by geographic locations or non-geographic attributes such aseconomic and/or socioeconomic attributes. For example, an individualsub-network may be organized on a geographical attribute such as amountain range or peak, a river basin or other valley, an aqueduct, acoastline, a canal system, a flight corridor, a common trade route, anairport, a major building, a power plant, a sports stadium, etc. Thesub-networks may alternatively be organized on economic attributes ordata collections, such as credit card computer server network or otherfinancial record computer database networks (e.g., banking andinvestment account databases), an automatic teller machine (ATM)network, a mobile communication network, an on-line service computerserver network for services such as Internet search services, socialnetworking services, video services, file sharing services, on-lineretail services, on-line auction services, on-line music services,on-line data storage services, on-line photo services, and on-line videoservices. The sub-networks may include a mixture of sub-networks ofdifferent attributes including the geographic location based subnetworks and non-geographic location based sub networks. Althoughsecurity network 10 is described herein as a four-tier network it shouldbe appreciated that any number of tiers may be utilized to construct themulti-tiered security network.

Each tier sub network may include a corresponding command center. Thecommand centers can include key controls, displays and command equipmentthat provide real-time intelligence information for criticaldecision-making. In particular, each command center is provided with areasoning agent that utilizes episodic memory to assess potentialthreats.

In an embodiment, security network 10 is an overarching network thatcoordinates multiple tiers of networks that span a particular coveragearea of interest, e.g., a nationwide coverage. Security network 10coordinates the efforts by providing nationwide communicationscapabilities, such as by linking the sub-networks via one or moresatellites 11 or satellite constellations. Security network 10 includesa first tier command center 18 that is assigned national scope tasks.For example, command center 18 provides the key controls forcoordinating communication amongst the lower tiered networks.Additionally, command center may provide displays and command equipmentfor organizing and initiating a national scope response, such asdeployment of National Guard resources, or restriction of borderthoroughfares. For example, command center 18 may be the NationalIncidence Management System—Emergency Operations Center (NIMS-EOC). FIG.1 shows one first tier command center 18 as an example. Two or morefirst tier command centers 18 may be deployed to generate redundancy inthe system.

Second tier networks 12 may be regional networks. For example, secondtier networks 12 may be organized by state, or by a nationwide grid.Second tier networks 12 may include third tier networks 16, fourth tiernetworks 14 as well as independent second tier stations 24.

Second tier command centers 20 are assigned second tier tasks, such asregionally based tasks, and may be included in each of second tiernetworks 12. For example, a regional network may include and be incommunication with a Critical Incident Response (CIR) regional commandcenter. Each regional network is assigned region-specific tasks such asmonitoring and controlling borders and communicating regional emergencysituation information to first tier command center 18.

Third tier networks 14 are provided having a different scope than secondtier networks 12. It should be appreciated that the scope of third tiernetwork\ 14 may be selected to be fully encompassed by second tiernetworks 12, or, in an alternative configuration, the scope of thirdtier network 14 intersects with second tier networks 12, as shown inFIG. 1. In this example, the third and fourth tier networks 14 and 16are shown to share some common network nodes (sensor nodes 26). Forexample, third tier networks 14 may be metropolitan networks or corridornetworks. Third tier networks 14 may include subsets of second tiernetwork components or they may cross over and integrate components frommultiple second tier networks 12. The third tier network 14 may includesecond tier stations 24 and/or fourth tier stations 26, as well asindependent third tier stations 28. In such an exemplary embodiment,third tier networks 14 may be correspond to major thoroughfares, such asan interstate highway. The stations included in third tier network 14are assigned sub-network specific tasks, such as tasks specific to theparticular corridor.

In FIG. 1, the third tier network 14 extends through a plurality ofsecond tier networks and utilizes second, third and fourth tier stations(24, 26 and 28, respectively). The intersection of third tier network 14with multiple second tier networks 12 and fourth tier networks 16provides an advantage in that communications that would otherwiserequire handoffs between sub-networks may be easily routed through asingle third tier network 14. A third tier command center 22 may also beprovided that coordinates the activities and communications between thestations that make up third tier network 14.

The network in FIG. 1 also includes local sub-networks, fourth tiernetworks 16. Fourth tier networks 16 can be constructed from a pluralityof fourth tier stations 26, or fourth tier nodes. Similar to the higherlevel sub-networks, fourth tier network 16 may include a fourth tiercommand center 23 if desired. Multiple fourth tier networks 16 may betied directly to third tier command center 22 or second tier commandcenter 20. Fourth tier stations 26 may be located at any locality, suchas a traffic intersection or toll booth and are generally configured toprovide the first indication of a possible emergency event unfolding.Fourth tier stations 26 may additionally, or alternatively, be providedin mobile forms, such as airplanes, boats, and/or automobiles.

The communications in the multi-tier network 10 in FIG. 1 can be invarious configurations. In one configuration, for example, any commandcenter in a tier sub network can access any node in that tier or a lowertier. For example, FIG. 1 shows that the first tier command center 18can directly communicate with the second tier command centers 20 and acommand center 14 in a third tier sub network 14 by bypassing the secondtier. Notably, in this configuration, the command center 18 can directlycommunicate with any node 26 in the network 10 to control the operationsof the node 26 and to collect data from the node 26 via any availablecommunication link between the command center 18 and the node 26.

FIG. 2 is a schematic of the security network 10 of FIG. 1 in oneimplementation of a multi-tier configuration. This example shows thateach lower tier command center is subordinate to a higher tier commandcenter and all nodes are organized in a networked computer hierarchicalarchitecture. Each computer is considered as a node of the system. Thehierarchy is comprised of several levels, beginning with a single nodeat the top of the hierarchy. The topmost node 18 is connected to a setof nodes in the level below it, in a one-to-many relationship: for onenational node 18, there is a set of regional nodes 20. Likewise, foreach regional node 20, there is a set of metropolitan nodes 22 below it,again in a one-to-many relationship. Each metropolitan node 22 isconnected to a set of local nodes 23, serving a portion of ametropolitan area. Each local node 23 is connected to a set of so-calledintersection nodes 26 which may be located at roadway intersections, forexample. Additional sensor nodes at the same hierarchical level as theintersection nodes 26 may also be deployed, serving similar functionsbut not located at traffic intersections. Each intersection node 26, orsensor node is connected to one or more sensors 30, through which itgathers data. Additional mobile nodes 26 can be included as part of thesystem, which can be carried by emergency services and responderagencies. These include hand-held, ground-based, vehicle-based nodes,and nodes carried by aircraft, e.g., police helicopters, etc.

In one configuration, a tier command center can communicate with anynode below it. For example, the region node command center 20 maydirectly communicate with a node 26 to collect data from the node 26 andto send commands to the node 26.

Referring to FIG. 3, the local sub-network 16 includes a plurality offourth tier stations 26. Each of fourth tier stations 26 communicateswith others of the stations over a communications grid that may includeboth wireless links 29 and wired links 31. Each of fourth tier stations26 may be either stationary or mobile and they may include one or moresensors 30. The plurality of stations can be provided with directcommunication to a local command center 23, which may also be referredto as an Executive I node. Local command center 23 can be provided withdirect communication with a higher level command center such as secondtier command center 20 or a third tier command center 22.

In one implementation, primary, secondary and redundant communicationlinks can be provided. For example, the network can be constructed toallow simultaneous communication to and from multiple tiers, i.e., thetier-based organization of the network does not result in hierarchicalcommunication restrictions. The communication links may rely onsatellite communications via various satellite constellations.Furthermore, wireless communications are provided between desiredcommand centers and desired networks. For example, wirelesscommunications may be provided over SIEMENS MC75 wireless communicationsmodules. It should be appreciated that any form of satellite, mobileand/or landline communications systems may be incorporated.

Each command center may have a network of distributed stealthy andnon-stealthy resources for monitoring and analyzing data. For example,local or distant (e.g., satellite-based sensor technologies) sensors maybe used to gather data. In the present embodiment, second tier commandcenters 20 control resources anchored to the nation's transportationsystem to perform data and intelligence collection in that region. Thestealthy resources can include sensors for detecting one or morehazardous materials, such as CBRNE materials.

In an embodiment, fourth tier network 16 of security network 10 iscomprised of the nation's roadway traffic controllers. All major U.S.cities have a traffic light system infrastructure that comprises manyidentical controllers. SIEMENS, for example, is a worldwide presence andis estimated to control over 1000 traffic control centers and over170,000 controllers in 78 countries. As controller technology hasadvanced, the traffic light system infrastructure has incidentally beenprovided with increased excess processing capacity that far exceeds theneeds of simple traffic control. In an embodiment, a network securitysystem utilizes, at least in part, the excess capacity of traffic signalcontrollers, that are already located throughout local roads, as well asinterstate highway on-ramps and off-ramps. Such a system may besupplemented with additional processing capacity if desired.

FIG. 4 illustrates an example of the local sub-network 16 that isorganized around a shipping port 36 and provides zonal defense of ahigh-value target. In this example, shipping port 36 is identified as ahigh-value target and a plurality of stations 26 are dispersed aboutshipping port 36 for assessing potential threats upon the target. Inparticular, stations 26 are dispersed at intersections of roadways thatlead to shipping port 36. In the illustrated embodiment, the density ofstations 26 varies in relation to the distance from shipping port 36.For example, stations 26 are more widely distributed further away fromshipping port 26. Fourth tier command center 23 is employed and is incommunication with stations 26. It should be appreciated that in thepresent embodiment a mobile station 26 may be incorporated into a boatso that shipping port 36 may be protected from water-borne attacks.

In another embodiment, a sub-network may be distributed over a highwaythat is located adjacent a high-value target such as power plant 37,shown in FIG. 5. In particular, the illustrated embodiment illustratesincorporation into a corridor that includes controllable choke points.The sub-network is incorporated into a larger security network as afourth tier network 16, but it should be appreciated that it may beincorporated as a third tier corridor network 14 if desired. Thesub-network includes a plurality of fourth tier stations 26 distributedalong an interstate highway that passes adjacent to power plant 37. Inthe present embodiment, a station 26 on either side of power plant 37may be provided with traffic signals or blockades so that in the even ofa detected threat approaching on the highway, traffic may be halted ineither or both directions. A fourth tier command center 23 is alsoprovided that may be used to control the actions of stations 26 as wellas to analyze data collected by stations 26 or to publish analysesresults to the higher level sub-networks. Stations 26 and command center23 may be incorporated into existing systems, such as truck weighstations and border checkpoints. Similar to the previously describedembodiment, power plant 37 is located adjacent to a large body of waterand as a result, the sub-network may include mobile stations such asboats.

In yet another embodiment, shown in FIG. 6, a portion of a securitynetwork may be organized on a major metropolitan area and providedefense by monitoring and controlling entry roads. For example, aplurality of fourth tier networks 16 and third tier networks 14 mayintersect at or near a major city. Third tier networks 14 may beorganized along major arterial roads such as interstate highways and/ormetropolitan areas. Stations may be included at particular areas ofinterest such as airport 38, and/or in traffic controllers or at anyother desired location. A plurality of third tier stations 28, secondtier stations 24, fourth tier stations 26 and various command centers,such as command center 23, may be included.

A sensor node 26 can be implemented in various configurations. A sensornode 26 can include a node module that includes a microprocessor thatrun a set of node module software tools for various node operations, aninterface with one or more sensors, and a communication module forcommunicating with other nodes 26, a local command center 23 or a highertier node in the network.

FIG. 7A shows one example of a sensor node module 26 that includes asingle-board computer to run node software, an IP addressable DAC/ADCinterface to one or more IP addressable sensors such as cameras, sensorinterfaces to other sensors and a communication module forcommunications. FIG. 7B shows an implementation of the sensor nodemodule 26 in FIG. 7A. The embedded node software installed in thecomputer includes data compression software, local analytics softwarefor analyzing sensor data, node database for storing sensor data,communication software, sensor interface software and node interfacesoftware. The communication module hardware include electronics forTCP/IP, WiFi, and wireless mobile communication ports. As shown in FIG.7A, the sensor module 26 may include a shell unit to enclose allcomponents and may be a waterproof shell. The sensor module 26 includesa power supply for powering the module. This power supply may be an ACpower suppler connected to the power grid or a portable power sourcesuch as a battery.

FIG. 8 shows an exemplary fourth tier station 26 utilizing a roadwaytraffic controller located at a roadway intersection. In this example,fourth tier station 26 comprises sensors 30, at least one controller 32and communications devices 34. Sensors 30 can be configured fordetecting various parameters. Depending on the sensing needs at aparticular location, various different sensors may be deployed at thelocation and such sensors can operate in concert to provide sensing datato a sensor node.

The U.S. Department of Homeland Security recently issued capabilityspecific national priorities to strengthen the nation's defenses againstthreats. The priorities include: 1) strengthening information sharingand collaboration capabilities; 2) strengthening interoperablecommunications capabilities; 3) strengthening chemical, biological,radiological, nuclear and explosives (CBRNE) detection, response anddecontamination capabilities; and 4) strengthening medical surge andmass prophylaxis capabilities. The Secretary of Homeland Security wasalso tasked with developing and administering a National IncidentManagement System (NIMS) which is intended to provide a consistentnationwide template to enable all government, private-sector, andnongovernmental organizations to work together during domesticincidents. The sensors 30 can be configured to detect CBRNE materials.The sensors may be configured to detect the presence of those hazards bysampling any gaseous, liquid or solid material. For example, chemicalsensors that may be used include solid-state gas sensors that measure aphysical property changed by a reaction at the surface, solidelectrolytes that measure electrical conductivity changes, catalyticsensors that measure temperature change due to heat of reaction at thesurface, ion mobility mass spectrometers and liquid crystal displaysensors. Biological sensors may include, among others, bioluminescencesensors, optical sensors, mass sensors, electrochemical sensors, quantumdot technology sensors, sensors utilizing dielectrophoretic techniques,sensors utilizing acoustic lysing or other microbial excitation,membrane technology sensors or sensors utilizing any other technologywhich detects and/or defines a threat organism. Examples of radioactivematerial sensors that may be utilized include proportional counters,Geiger-Muller counters, and Reiter-Stokes ionization chambers sensors.Examples of nuclear sensors include low background detectors, neutrondetectors and alpha/beta/gamma detectors. In addition, sensors forexplosive materials may include ion mobility spectrometers and directsampling ion trap mass spectrometers. It should further be appreciatedthat various other sensors and sensor technology may be incorporatedsuch as muon tomography, direct x-ray, backscatter x-ray, gamma-rayimaging, advanced spectroscopic portal technology, nuclear resonancefluorescence imaging, hyper-spectral imaging, etc. In some applications,the sensors can be incorporated in security network 10 to providepassive detection so that their presence is not detectable. As mentionedpreviously, any sensor may be utilized and the types of sensors are notlimited by their proximity to the monitored location. For example,distant sensors, such as satellite based sensors, may be utilized tomonitor and may be incorporated into stations included on any tier.

Sensors 30 used in the system can also include biomechanical sensors(breath signature, heart rate, etc.) to determine threat relatedmanifestations; biometrics to identify the individual of interest(facial recognition, gait analysis, vein pattern, iris/retinal scans,fingerprinting, etc.) by comparison to a known database; and behavioralassessment techniques as provided by baseline optical or other sensordata and subsequent machine interpretation defining behavior ofinterest. The system can also be configured to include contrabandsensors as part of the sensors 30 to target contraband in addition toCBRNE targets.

Additionally, the regional command centers may also have control ofnon-stealthy resources anchored to the nation's transportation system.Such non-stealthy resource may include, but are not limited to,weather-monitoring devices such as Doppler radar systems, precipitationgauges, anemometers, thermometers, barometers, hygrometers, barographs,etc. Additionally, they may include other non-stealthy surveillanceresources such as cameras and their related data transduction orinterpretation capabilities that may be used to perform license platerecognition, facial recognition, or behavior recognition. Suchrecognition features may be used to track known terrorists and criminalson the public roadways.

Controller 32 includes a processor that is provided with reasoning logicthat provides autonomous and automatic data processing and analysis. Asa result, the network does not rely on a centralized processing hub tosort and process data collected by all of the data sources. Instead,each processor may be independently capable of sorting and analyzingdata, making predictions and designing preemptive plans. The preemptiveplans may then be sent to second tier command center 20 or first tiercommand center 18 so that the plans may be coordinated to reduceresponse times and to avoid conflicting routing of resources.

The sensing mechanism in the system can include video analytics toanalyze video data to detect whether the same vehicle or person has beenfrequenting a high-value target location such as a nuclear power plant.The system can be configured to determine if this is innocent activityor if the represents a potential threat. The system compares itsinformation against various watch-lists for a match. The system isinstalled at nuclear plants and other high-value targets in other partsof the country. In operation, the system will try to link dataassociated with the vehicle or person to similar events at the other GKinstallations. A threat confidence level will be determined. If thethreat exceeds a predetermined series of thresholds, the system willidentify the threat and signal appropriately.

FIG. 9 shows one exemplary implementation of the multi-tier system inFIG. 1 in connection with various computer systems that are connected tothe system. The sensor nodes 26 as shown include one or more AutomatedLicense Plate Recognition (ALPR) systems, one or more cargo containerdetectors such as radioactive detectors (e.g., Geiger-Muller countersand others), and first responder mobile sensor nodes. Other sensornetworks and databases may also be connected to the system to allow themulti-tier network to collect and analyze data from such sensor networksand databases. Other computer systems at the metropolitan level, theregional level and the national level may be installed with propersoftware modules to interact with the system.

FIGS. 10-13 show examples of node structures in the multi-tier network10. The stations and command centers included in security network 10rely on a common operating environment that provides an interfacebetween the nodes of the multi-tiered security network. The operatingenvironment is distributed to each component of the network so thatevery component is compatible with the other components and so that theymay share processing capabilities. The operating environment can bedriven by a discrete set of specific user configuration components tobecome a specialized operating environment for a particular tier ofcommand and control. As a result, although all components share a commonoperating environment, the environment may include aspects specificallydesigned for the respective problem-set of that tier.

The operating environment is optimized based on those particular problemsets and, as a result, each layer has a specifically designated set oftasks to process. Therefore, the system can optimize the use ofprocessing power in the system by distributing specific tasks toprocessors included in the nodes of a specific tier. Additionally, thestructure reduces the communication network traffic by sending analysisresults rather than raw data in the first instance. The next higher-tiernetwork, however, may request the raw data so that a reasoning modulemay be updated if an emergency situation develops.

FIG. 10 shows an exemplary implementation of the multi-tier system 10where the sensor nodes 26 are intersection nodes at differentintersections of roadways. Each sensor node 26 is connected to one ormore node sensors to collect sensor data and includes local database tostore sensor data and local analytics to analyze the sensor data. Inthis example, the sensor nodes 26 are connected in a mesh network whichis connected to a fourth tier executive node 23. Third party databaseslabeled as “stakeholder databases” are connected to higher tier nodes22, 20 and 18 in this example. In addition, authorized or registeredsystem users can access the system via the nodes 22, 20 and 18 viasystem portal browser interface installed on user computers. Authorizedor registered system users can also access the system through nodes 22,20 and 18 via mobile web interface via mobile communication devices suchas cell phones, PDAs and mobile computers.

In this example, the fourth tier stations 26 provide the lowest levelnode. In various embodiments, the fourth tier stations correspond toroadway intersection controllers, i.e., intersection nodes. Theintersection nodes are generally tasked with tier specific tasks such assensor control, data collection, data processing and data analysis, andcommunications with higher level tiers.

In FIG. 10, the higher tier commend center nodes 23, 22, 20 and 21 areshown to include various data collection, processing, analysis anddecision functions. The node 23, for example, has its database forstoring data, a sensor fusion module for aggregate received sensor datafrom different sensor nodes 26, and a first-order reasoning module toanalyze received data. Nodes 22, 20 and 23 include portal web serversfor interfacing with various system users and third party databases andother systems. Nodes 22, 20 and 23 also include respective analyticsmodules for data analysis and situation awareness modules to monitorvarious system state variables.

FIG. 11 shows an example of the intersection node 26. This intersectionnode 26 includes a module manager 110, a database 112 and communicationsmanager 114. Module manager 110 includes a plurality of modules 111 thatprovide various functions. For example, modules 111 may provide a userinterface, an intersection node manager, various analytics (such as adata warehouse module), multi-modal sensor fusion, complex reasoning,etc. Each of modules 111 may write to or draw information from database112 to provide the desired function. The communications manager 114generally includes interfaces 115 that provide communication betweenvarious sensors 30 and module manager 110 so that the gathered data maybe analyzed. Additionally, communications manager 114 provides aninterface for other nodes included in the sub-network. The interfacesprovide data to module manager 110 in compliance with a desiredcommunications protocol, such as distributed data services (DDS) ormicro-DDS, that allows each of the stations and command centers toeasily communicate with each other.

In operation, the intersection nodes 26 can be tasked with eventdetection, first order reasoning, threat classification and localizedsurveillance. Each intersection node collects data from sensors, such asCBRNE sensors, and monitors the data for a triggering event. The sensorscollect data and the station analyzes the data for threshold amounts ofthe CBRNE materials. The station can maintain a record of both above andbelow threshold readings. In the event a station detects a greater thanthreshold value of such a material that determination is published tothe associated command center. If that station detects a lower thanthreshold value, additional sensor data may be analyzed or requestedfrom other intersection nodes. The station may be capable of collectingand analyzing any type of desired data including, but not limited toCBRNE sensor data, video data, audio data, environmental data, etc. Inaddition, the station may be provided with a cross-reference database ora communication link to a cross-reference database so that fourth tierstation 26 is capable of providing a wider range of analyses such asvideo recognition analysis.

Referring to FIG. 10, the stations 26 are assigned to a fourth tiercommand center 23. Command center 23 coordinates and manages stations26, receives data and analyzes results from the associated stations 26and publishes information, such as alerts, to an overarchingcommunications system that links command center 23 and the higher levelcommand centers, such as regional command center 20 and national commandcenter 18 and distributes information published by other command centersto stations 26 within its control.

Similar to the lower level intersection nodes, command center 23includes a module manager 120, one or more databases 122 andcommunications manager 124, as shown in FIG. 12. Module manager 120includes a plurality of modules 121 such as a user interface module, anintersection node manager, various analytics (such as a data warehousemodule), multi-modal sensor fusion, complex reasoning, etc. Databases122 may include any permanent or temporary memory device or combinationsthereof. Communications manager 124 generally provides an interface 125between the command center 23 and nodes that make up the sub-network. Asmentioned above, interface 125 allows command center 23 to communicateefficiently with associated nodes over a common communication protocol.

Command center 23 can be tasked with disseminating local situationawareness to users and higher level network components. In addition,command center 23 performs higher-order analytics on data and alertspublished by stations 26. Command center 23 may also be in communicationwith local stakeholder databases that may be used in the higher-orderanalytics. For example, command center 23 may provide traffic flowanalysis and may be linked to local roadway databases and may combinethat information with sensor data and controls provided by stations 26to alter traffic patterns so that potential response routes may becleared, a detected threat may be stalled and/or traffic may be divertedfrom a threat.

The next higher tier sub-network, which includes command center 20,provides regional scope capabilities. The regional command center 20 canbe configured to perform regional incident management and provideslarge-scale situation awareness, in addition to higher-order analytics,database maintenance, and communications between adjacent tiers andregional stakeholder databases.

Referring to FIG. 13, command center 20 is constructed from modulemanager 132, databases 132 and communications manager 134. Modulemanager 130 includes a plurality of modules such as a user interfacemodule, a node manager for managing information from the lower leveltiers, analytics (such as a data warehouse module), multi-modal sensorfusion, complex reasoning, situation awareness, alert/incidentcoordinator, etc. Databases 132 may provide permanent or temporarystorage and may include cross-reference databases and/or storage spacefor the analytics module included in module manager. The communicationsmanager 134 includes various communications interfaces 135 that allowcommand center 20 to communicate with users, nodes of the same or othertiers, and first responders. In particular, interfaces 135 may include auser interface, an inter-nodal interface, TCP/IP non-DDS interfaces, avoice-over-internet protocol interface, a radio interface, etc. Itshould be appreciated that any industry standard data sharing protocolmay be employed, such as National Information Exchange model (NIEM),Emergency Data Exchange Language (EDXL), or IEEE 11512 for example.Additionally, communications manager 134 may provide communications witha storage area network 136.

The command center 18 provides command and control over the entiresecurity network 10. It provides incident management, large-scalesituation awareness, higher-order analytics and includes one or moredatabases and communications management. Command center 18 may also belinked to major stakeholder databases, such as Interpol. Command center18 may also distribute data mining tasks to lower-tiered commandcenters, such as review of financial records, motor vehicle records, andadditional detailed video data gathering. In one implementation, thecommand center 18 may be constructed with architecture identical orsimilar to that shown in FIG. 13. The operating environment of securitynetwork 10 includes display modules, or engines, that create a visualenvironment that decision makers may use to gain full real-timesituational awareness. The display modules provide means to maximize thevisualization of a variety of fused multi-sensor data types. The displaymodules integrate geo-positioned and conventional camera source video,computer generated imagery, display technologies and relational databasemanagement tools. The combined display allows the user to makeassessments, operate and shape the dynamics of the problem as well asexperiment with various contingencies. For example, a simulation moduleis included that allows a user to simulate a scenario and to testcounter-measures, skills and judgments. Various modules that may beincluded are mapping modules, three dimensional modeling modules,traffic flow modules, responder tracking modules, impact predictionmodules, etc.

Examples of various mapping displays are illustrated in FIGS. 14 and 15.Displays may be provided including street maps and/or satellite images.Additionally those displays may include overlays of the stations, ornodes, included in the security network. Icons also may be provided thatprovide a real-time simulation of movement of a vehicle of interestincluding information specific to the vehicle. As a further alternative,traffic flow indicators may be provided and combined with the street mapor satellite images.

The display modules are configured to create the visual environment onany display device, such as conventional monitors, advanced sphericalimmersive environment monitors that are customized for the desiredvisual environment or hand held wireless devices carried by firstresponders.

Display modules may also be incorporated into remote devices for usersthat are in the field. For example, any computing device may be utilizedby decision makers to assess, command, operate and shape the dynamics ofthe problem. The decision makers are also able to experiment withvarious contingencies to test counter-measures, skills and judgmentsindependent of the command center.

The virtual environments created by the display modules are suited forobserving, planning, intelligence preparation, real-time visualizationof the problem-space with common operational and tactical pictures,intelligence surveillance reconnaissance asset positioning and routeoptimization, and problem or exercise reconstruction for after actionreview.

The security network operating environment relies upon an episodicmemory to improve response time. As described above the system includesanalysis and complex reasoning modules, which may communicate with oneor more databases that provides a library of historic and/or theoreticalemergency scenarios, or simulation templates. The network utilizes thelibrary of scenarios to compare data to assess a likelihood ofoccurrence of the particular scenario. After the likelihood has beenassessed, the system determines if the likelihood analysis warrantspassing those analysis results to other networks on the same ordifferent tier.

The analysis and complex reasoning modules provide continuousinvestigation of data, digital image watermarking, apply algorithms anddecoding performance metrics, apply algorithms that allow for discretemessage encoding and other linear and non-linear relationships, andsupport full processing, cluster searches, association/recall,prediction, statistics, filtering and animated system state monitoring.

The analysis and complex reasoning modules can utilize agent-basedcomputing and control. They are capable of predicting behaviors andapplying a confidence value to the predicted behavior based on thecorrelation with the known simulation templates. The application of aconfidence value may occur at any tier level. For example, a confidencevalue may be applied to a sensor detection analysis, e.g., confidencewith which a muon tomography, license plate or facial recognition scanprovides a match, or at a larger regional level, e.g., confidence withwhich a known scenario is unfolding. Furthermore, the analysis andcomplex reasoning modules are capable of performing an iterative processwherein they make predictions and reassess earlier predictions based onupdated data gathered from the sensors and user inputs to adjust anexisting simulation template or to create new simulation templates.

The analysis and complex reasoning modules may provide episodic memoryat various scales. An example of local scale operation is quicklyidentifying items of cargo. As mentioned above, sensors 30 at selectedlocations in the network 10 may include muon tomography sensors. Muontomography is a technique that may be used to examine the insides ofopaque objects using the ambient flux of cosmic ray charge particles(predominately muons). In particular, the trajectory of muons is trackedprior to, and after, passing through an object. Based on the measuredscattering of the muons, the density of articles in the object can bedetermined. Muon tomography may also be used to perform shapeidentification, with an identified confidence level, based on a definedlibrary of ordnance. Radiological and nuclear materials are generallytransported in shielding materials that have extremely high density andtherefore are easily detected through muon tomography.

The inclusion of a muon detector in the present multi-tier system 10 maybe through a system adapter. The system adapter may provide connectionwith other sensors, communications to the Network, datastorage/retrieval, logging, user interfaces, additional analytics,referential data bases, system diagnostics and maintenance features. Theadapter will monitor numerous GK parameters for traits that are out ofan expected range and provide for intervention where appropriate. Thismonitoring includes both system performance and threat analysis.Examples of system performance analysis are monitoring gamma and neutronbackground values. Examples of threat analysis include performingstatistical analysis on stored data based upon shipper, receiver,container contents, and driver ID to identify detector levels that trendaway from norms. When an alert is determined by the Muon detector, theAdapter will implement the Alert protocol specific to the installation.

The analysis and complex reasoning modules along with associateddatabases may be utilized to quickly assess the likelihood of aterrorist threat based on known muon tomography signatures at a locallevel. For example, through muon tomography an object having a highmolecular mass may be detected and the dimensions may be approximated.The high molecular mass may be associated with shielding materials usedfor radioactive materials. The approximate size and the determinedmolecular mass value may be compared to known radioactive materialsources, through simulation templates, to anticipate a likelihood of thepresence of such a material.

The multi-tier network 10 include data storage devices or severs atevery node and at every tier to provide a distributed data storagethroughout the system. FIG. 16 illustrates the data flows in a localsensor node 26 in the multi-tier system 10. In this example, sensor datais collected from one or more sensors and stored as the raw video data,other raw sensor data, and various data. Part of the data can beforwarded to a corresponding higher node 23, in response to a request,for storage in a permanent archival storage device or server. The datastorage devices in the node 25 may be first in and first out (FIFO)memories designed based on specific data retention periods based on thesystem requirements.

FIG. 17 shows an example of data flows in the entire multi-tier system10. Initial raw data is collected through sensors, store locally andforwarded to higher-level nodes. Sensor data is analyzed, compared withlocally stored data, compared with data of external origin, converted tomore useful information, managed as incident tracking, and stored at theclose of an incident for historical and forensic purposes. Data alsoflows into and through the system from outside sources, entering thesystem as watch-list items, alerts, and in other forms. Data is movedthrough the system by the system users, operators, and stakeholders inthe service of the primary use cases of the system.

FIG. 18 shows a particular example of data flows in the multi-tiersystem 10. A sensor detects a known kind of threat at a trafficintersection and forwards the data to its intersection node 26 (1). Thenode 26 generates an alert, and immediately publishes the alert, whichis picked up by its neighbors 26, which go to a higher surveillancelevel by lowering the detection threshold by a selected amount so thatneighbors 26 operate in a more sensitive mode (2). The nodes 26 forwardtheir collected sensor data to their superior tier node 23 (3). Thisnode 23 fuses the sensor data, and performs analytic and reasoningprocesses on the data (4). The node 23 autonomously collects externaldata on the vehicle of interest and the person of interest for matching,further identification, and confirmation of the threat.

Next, the node 23 generates a package of data and information,characterized as an Incident, and forward such data to the next highernodal level, the node 22, for further analytic and reasoning treatment(5). A system watch officer picks up the Incident on a workstationinterface, and begins to overlay the autonomous processes with humaninput (6). The watch officer conducts further resources on external webservices available through the Workstation interface, enhancing theIncident information package (7). The watch officer returns the Incidentto the node 22 for further action (8). At this time, the node 22 reviewsthe Incident against appropriate criteria and generates a Response Plan,which is published onto the system (9). The response plan is availableto the watch officer and stakeholders.

Finally, the Response Plan is forwarded to all the remaining levels ofnodes 20 and the first tier node 18. The further handling of theIncident, including the execution of the Response Plan, is implementedin the system.

FIG. 19 shows another example of data flows in the multi-tier system 10when a muon detector identifies a potential threat at a check point. Thesystem responds to the alert by increasing the system awareness at thenode 26 where the threat is detected and its neighboring nodes 26. Thethird tier node 22 also is also put on an awareness state and informsthe regional node of the detected situation.

In one implementation, the system can be implemented to in a way inwhich data is acquired and retained at nodes 26. In this design, apermanent memory device is provided in the node 26 to store the data.That data may be structured at the node 26 or remain unstructured. Initself, the data may be meaningless, however, t is archived in a mannerthat is searchable by the analytics resident in the node 26. Aninvestigation at another node can call on the end node 26 to search itsdata for specific information and return meaningful information. Forinstance, an end node 26 with a video camera may capture, throughAutomatic License Plate Recognition, all vehicles that pass thru it'sfield of view surrounding a nuclear power plant and locally store thisdata in flat files or a relational data base. An investigation occurringat another node may be interested in knowing if the end node hadencountered a particular license plate. The investigative node can senda query to one or more nodes seeking hits on the vehicle license plate.If a hit occurs, the node might return the date, time of the occurrencealong with video of the vehicle and passengers. Similar distributed dataacquisition, storage, analytics and communications could occur withfacial recognition, vehicle recognition and other behaviors and objectsof interest. A system could be configured to allow the pass-thru of datato other nodes. In that system configuration the data storage, analyticsand communication could occur at multiple levels. The system might alsobe configured to maintain the data isolated from other GK Nodes, takingon meaning only within the context of an investigation or criminalalert. This configuration could be implemented to maximize the privacyprotections of the public.

Notably, the multi-tier system 10 is designed to provide automated datacollection and analysis and to produce automated system warning andcertain responses. In addition, the system 10 provide user interfaces toallow various system operators, decision makers and other system usersto interact with the system 10. The system software may be configured toallow a decision maker to override certain automated system operationswithin a given authority level. FIGS. 20 and 21 show various systemusers at different tier levels.

FIGS. 22-25 shows different operational scenarios in a multi-tier system40 to illustrate the system operations. Operation of a security network40 will be described in relation to a theoretical event. A portion ofsecurity network 40 is illustrated in FIGS. 22-25. Security network 40is a first tier network that includes a plurality of second, third andfourth tier networks. All of the sub-networks operate on a commonoperating environment but are assigned tier-specific tasks.

As described previously, second tier networks may be regional networks.In the present embodiment, second tier networks, such as regionalnetworks 42, 44 and 46, are included and encompass portions of thirdtier networks, such as corridor networks 48, 50, 52 and aqueduct network54, and a plurality of fourth tier networks, such as local networks56-72.

Regional network 42 encompasses local networks 56, 58 and 72, and aportions of corridor network 52 and aqueduct network 54. Regionalnetwork 44 comprises local networks 60, 62, 64 and portions of corridornetworks 48 and 50. Regional network 46 encompasses local networks 66and 68, and portions of corridor network 50 and aqueduct network 54. Itshould be appreciated that the local networks may be organized aroundany local attribute. For example, local networks 56, 58, 60, 64 and 68are generally organized within local roadways and utilize roadwaytraffic controllers to provide the fourth tier stations. The localnetworks may also be organized at places of particular interest, such asa downtown, e.g., local network 62, or a sports stadium, e.g., localnetwork 66.

Referring first to FIG. 22, in one scenario a detector agent at fourthtier station 74, an intersection node, detects a vehicle carrying ahazardous chemical, e.g., a recognized threat or vehicle of interest(VOI) 41, with high confidence by analyzing data collected with achemical sensor at that station. The detector agent classifies thematerial based on data samples and determines the confidence level basedon the data gathered and its similarity to known simulation templates,i.e., it relies on episodic memory. The chemical classification andconfidence value is communicated to higher order agents, i.e.,components in higher tier sub-networks and the national command center,so that higher level situation awareness may be initiated.

Additionally, an alert is distributed to other stations on the same tiersuch as stations 75 and 76, which are in a forecast path of travel ofVOI 41, so that the other stations may continue to collect specific datatypes. For example, after station 74 has determined the presence of achemical, stations 75 and 76 may focus on other aspects such as facialrecognition of the occupants or physical attributes of VOI 41 such asdecals or other markings or those stations may be specifically requestedto confirm the chemical detection. Additionally, the nearby stations maybe utilized to alter traffic patterns to slow the progress of VOI 41and/or to divert traffic flow away from VOI 41. As a still furtheradditional feature, the sensitivity of any of the sensors at stationsadjacent or nearby station 74 may be modified in response to the initialdetection of the chemical or any other potential threat.

In the event station detects the presence of the chemical with lowconfidence, it may employ the other stations in the same tier to gatheradditional information in an attempt to increase the confidence level.For example, station 74 may detect a chemical but based on theconcentration detected it determines with low confidence that thechemical is a threat. Such a reading may be a result of a reduced scantime due to the traffic signal timing. Station would enter a log entryindicating that it was unable to complete the analysis and it would senda request to neighboring stations 75 and 76 to change the traffic flowtiming to slow the progress of VOI 41 so that chemical sensors at thoselocations may reassess the concentration of the chemical. The requestmay also be directed to additional sensors such as a video camera sothat facial recognition or license plate scans may be utilized tocorrelate gathered information with watch list databases, such as adatabase of people with known affiliations with terrorist networks or adatabase of stolen cars.

The detector agent queries additional sensors integrated at that trafficintersection, (e.g., video, weight sensors, velocity sensors) to gatheradditional information and to create a record of even tags. Additionalinformation collected may include the weight of the vehicle to determinethe quantity of the chemical, the license plate number to correlate withmotor vehicle records, video data to determine whether the vehicle iscarrying a person on a national or local watch list, and data related todirection and speed of travel.

After additional data is retrieved, detector agent of station 74continues to utilize its episodic memory capability and the integratedanalysis and complex reasoning modules to match the data to simulationtemplates. In this example, station 74 may determine that given the typeand quantity of chemical and vehicle occupants a likely scenario is thatthe chemical may be used as an explosive or as an environmentalcontaminant. Station 74 communicates this information in the form of analert to adjacent stations, such as stations 75 and 76 and the regionalcommand center 82. It should be appreciated that such a determinationmay be made at a higher tier command center if desired. For example,station 74 may pass the initial alert of the chemical detection to ahigher tier command center and that command center may correlate thechemical detection with likely scenarios through a higher level episodicmemory function.

Regional command center 82 relays the information to a national commandcenter as well as adjacent regional command centers 84, 86 and corridorcommand centers 88, 90 by publishing the alert on a system-wideknowledge message board. The national command center, as well as nearbyregional command centers 84, 86 and corridor command centers 88, 90 aredesignated in a subscriber list that results in the alert beingdisseminated to them through the message board.

At the regional level, command center 82 is tasked with predictinglikely routes, such as by identifying locations of interest, orhigh-value targets. For example, command center 82 may recognize that aterrorist carrying a high quantity of the chemical may target a downtownarea of a city, a nearby sports stadium, or a nearby water aqueduct. Inthe present scenario, regional command 82 determines that threat 41 ismoving in the direction of local station 75 and assigns a lowerprobability that the threat 41 is targeting downtown 62.

In addition to providing forecasting of likely routes and scenarios,various command centers may be tasked with looking to past eventsleading up to the current threat. As a result, the network is capable ofcross-correlating local, regional, National and/or International data toestablish linkage and incident relativity. For example, the nationalcommand center may initiate data-mining by searching credit carddatabases, bank record databases, travel records, ATM transactions,known affiliations and phone records, among others, of the identifiedoccupants. On a regional or local level, the security network may querythe regional and/or local sub-networks for additional information suchas past records of detection of the chemical at lower than thresholdlevels.

The data-mining activities may be used to build a forensic evidence logand to alert the system to related threats in other locations. Forexample, travel records may indicate that the occupants have traveled toother major cities in the recent past. Phone records may indicate thatthe occupants have also been in phone contact with other people locatedin those cities that are also included in a watch list. Through thecombination of that type of data, the system may forecast a higherlikelihood of a similar event transpiring in those other locations. As aresult, the system may reduce the threshold level associated with thedetected chemical for publishing an alert in those cities and reconsiderpast data, such as lower than threshold detections of the same chemicalas that carried in VOI 41.

Given the early warning by command center 82, command centers for theregions associated with the likely targets, in combination with anational command center, may begin staging responses and preemptivemeasures, such as by warning responders and altering traffic routes.Because the exemplary system utilizes traffic controllers at the locallevel, thoroughfares that are designated paths of responders may be keptclear by changing the timing of the traffic signals and traffic may bediverted away from VOI 41. The staging of the response assets is used toreduce response time of responders. Additionally, traffic controllers inthe forecast path of VOI 41 may be timed so that the progress of VOI 41is hindered to provide first responders additional time to stage and/orengage VOI 41.

Stations not directly in the path of VOI 41 may be utilized to poolprocessing resources to reduce the overall processing time required fordata gathered by stations that are directly in contact with threat 41.For example, video recognition processing of video data gathered atstation 74 may be distributed over other stations included in localnetwork 60, such as stations 77, 78, 79 and 80.

Referring to FIG. 23, security network 40 continues to track threat 41and has determined that threat has exited local network 60 and is nowtraveling through corridor network 48 at a high rate of speed.Communication between local network 60 and corridor network 48 issimplified because the communications are provided with a commoncommunications protocol and station 76 of local network 60 is also astation, or node, included in corridor network 48.

Corridor command center 88 of corridor network 48 performs third tierassigned tasks, such as predicting likely paths based on knownconditions through the corridor. Command center 88 may also determineand recommend response routes to the national command center in additionto command centers of sub-networks included in the likely path of threat41. For example, command center 88 may send warnings to command centers86, 84 and 94 that threat 41 is approaching at a high rate of speed andis likely targeting locations within their sub-networks. Each of thoseadjacent command centers may also query within their own network whetherany additional known terrorists are traveling through their networks ona convergent path with threat 41. Command center 88 also continues togather additional information and to track threat 41 utilizing corridorstations 76, 96, 97, 98 and 99.

Referring now to FIG. 24, corridor station 98 of corridor network 48determines that the path of travel of threat 41 has changed so thatthreat 41 has entered corridor network 52. Based on that change indirection, corridor command center 88 may alter the prediction initiallyprovided by regional command center 82 to reduce the likelihood that theaqueduct is the targeted location and increasing the probability thatthe sports stadium is the intended target of threat 41. Corridor commandcenter 88 relays this updated prediction to the national command centerin addition to the adjacent regions and corridors so that the processingpower of those sub-networks with having a lower probability of attackmay be reallocated for support or for normal operations.

Additionally, the updated prediction made by corridor command center 88may be utilized by regional command center 84 and corridor commandcenter 86 to initiate the staged responses and preemptive measures. Aspreviously mentioned, after the sports stadium, was identified as apossible target of threat 41, command center 84 began to stage aresponse and preemptive measures. As the probability of attack isincreased based on the path of threat, command center 84 initiates theresponse, as shown in FIG. 25, and because the early warning allowed forthe staging of the response, the response and preemptive measures may bemore quickly deployed to contain threat 41.

The ability of public safety and service agencies to talk within andacross entities and jurisdictions via radio and associatedcommunications systems for exchanging real time voice, data and/or videois a major issue in security networks. Because the security network ofthe present invention utilizes a common operating environment thatallocates tasks based on the tier of the network and utilizes commoncommunication protocols, communications are simplified and are notlimited by borders and jurisdiction.

Although, an exemplary embodiment of the security network has beendescribed that utilizes existing road traffic controllers, it should beappreciated that any network may be used. For example, dedicated networkmay be used in combination with existing networks that are primarilyutilized for other purposes.

Referring to FIG. 26, another scenario will be described. The scenarioillustrates a method of using a security network for monitoring aroadway intersection. In a first step 140 an intersection node collectscontinuous video surveillance. The video surveillance is utilized toperform automated license plate recognition, as shown in step 142, bycross-checking the vehicle identification with a watch list, as shown instep 144. Concurrently, an archive is maintained in step 146 of all thevehicle license identifications that are made by the intersection nodeto provide a historical record that may be utilized by the securitynetwork for data-mining if required.

In the event a match is made between a license identification and anidentification included on a watch list, alert and tracking proceduresare begun in step 148. The alert and tracking procedures may include aplurality of parallel activities. For example, multi-intersection videotracking and analysis 150 may be begun, which may include facial scansof the driver and passengers 151. Traffic flow may be managed 152 suchas by altering the traffic controller timing to slow the vehicle ofinterest or to divert traffic flow away from the vehicle. Additionally,a user display 154, 155 may be provided that presents event history,including the triggering event, and real-time situation feed and datamining results from data mining operations 156. As described previously,data mining 156 may include retrieval of vehicle owner information viamotor vehicle records, credit card records, bank records, phone records,travel records, etc. All of the information gathered by the intersectionnode is passed into the security network so that a preemptive plan maybe constructed and executed.

The present multi-tier system can be viewed as a large, complex statemachine and it may be difficult to specify a unified system state in itsentirety at any one time. Nonetheless, on every useful scale of thesystem structure, enough of the system state is known, and issufficiently accessible, to support effective operations. The state ofthe system is defined as the array of values of the system's statevariables at an instant in time.

Some of the system's state variables are especially intended to faceoutward, to express collectively the key conditions of the system. Thesevariables are used both as critical controls and control markers inoperations, as well as indicators of system health and other conditions.

These special outward-facing state variables are expressed throughhuman-accessible and system-accessible interfaces. Collectively we callthese variables, as expressed through these interfaces, the internalsystem awareness.

In order to operate as intended, the system may also need to take intoaccount a range of external factors in its operating environment, or inthe world at large, and reflect them for its own use as an array ofexternal state variables. Collectively these variables, expressedthrough appropriate interfaces, represent general situation awareness.The internal system awareness, and general situation awareness can beimplemented through a unified set of interfaces.

In addition, the system can be operated based on specific situationawareness related to the state of some external situation the system isdesigned to observe, or monitor, or analyze, or react to. Specificsituation awareness is typically described in terms of its scope, suchas local situation awareness, or large-scale situation awareness.

Consider an example of threat detection. A threat is detected directlyby a sensor at an intersection node. This is expressed as a set ofspecific system variables locally. An alert is generated, an incident isinitiated, and a cascade of system variables are triggered to specifichigher values. For example, a variable we may call surveillance level israised to a higher level, and the system begins scanning neighboringlocations more frequently than before, and saving scan informationlonger. The threat is published locally, and propagates to an executivenode which takes note of it. If the threat is sufficiently great, thegeo-spatial scope of the incident is increased. The scope of the threatis evaluated by the system and perhaps a human watch officer, and adetermination is made as to which level of the system should own theincident as it evolves. A comparison is made to information carried aspart of the system's general situation awareness, in the process ofshaping a response to the incident. As the incident progressed, thesystem performs analytics and reasoning—based on these values ofvariables—and creates a specific situation awareness that applies to theincident. And so on, each aspect of the operation carried by systemvariables, the awareness of which can be assessed by the system itself,or by an external system, or by human operators at any time. In the caseof this incident example, the threat may be deterred, and the values ofmany variables may return to their prior values.

In one implementation, the following parameters may be included aninventory of some of the key aspects of system awareness:

Temporal Processes: Heartbeat, Large-scale heartbeat, andTime-synchronization.

Steady-State Processes: Data collection and Data flow and storage.

Events and Episodic Processes: Sessions, Scans, Trackable Events,Incidents and Participation in externally managed events.

Scoping of Awareness: Geo-spatial scoping, Nodal scoping, zonalawareness, Scoping relative to a threat detection, and Scoping ofnotices and alerts.

Selective Awareness: Qualitative filtering, Quantitative filtering,Passive Filtering (use of system thresholds), and Active Filtering (setby a user).

Surveillance Level: Indexed value, based on a useful scale.

Surveillance Scope: Geo-spatial scope, Activity scope, System scope (athigher levels may invoke data flows from outside the GK system).

Threat Type and Level: Qualitative and quantitative assessment of threatdetected by the system.

Alert: a system flag triggered by detection of a threat, or a human userin response to a threat or other condition. Alerts can be used tocollectively trigger other variables to higher indexed levels as agroup.

Incidents, and Incident Level: An incident is a managed process,initiated automatically upon detection of a threat, or initiated by asystem user in response to some criteria. In GK, incidents are managedand tracked in compliance with the response planning and managementprocesses defined by the National Incident Management System (NIMS).

Incident Ownership: An incident is owned by the system at some level, orby an internal system user, or by an external stakeholder or systemuser. Ownership of an incident depends on incident scope, and is nature.Guidelines for incident management and ownership are also defined byNIMS. Incidents of sufficient importance are managed from an IncidentCommand Center, which may or may not be co-located with a Regional orNational Fusion Center.

Maintenance and Administrative Awareness: System Health, Administrationof nodes, Administration of network processes and User administration.

System Security Awareness: Users, user authentication, user authority,Network security and Hacker resistance.

Resource Availability: Data storage resources, Computational resources,Analytic and reasoning resources, External resources and Stand-byresources.

General External Awareness: World View, Awareness of specific kinds ofoutside events, and Pass-through awareness from participatingstakeholders and subscribing agencies.

In another aspect, the present multi-tier system is designed to processinformation autonomously, collecting data through its highly distributedmulti-modal sensor network and perform analytic and reasoning processeson such real-time sensor data. This real-time data is correlated withhistorical and intelligence based data from multiple sources to presentactionable Response Plans for autonomous or semi-autonomous initiationand execution.

Distributed intelligent software agents are embedded throughout themulti-tier architecture to perform persistent complex and collaborativereasoning on the real-time sensor data that is being ingested by thesystem. The intelligent agents are programmed with specific roles thatreflect a knowledge processing hierarchy and allow for cooperativeprocessing of sensor events as well as processing of higher order threatevents that resulted from logical inferences of data.

For example, FIG. 10 illustrates the distribution of informationprocessing with the lowest level sensor nodes 26 applying analytics onthe incoming data and the superordinate nodes 23, 22, 20, and 18applying a progression of analytics and complex reasoning to deriveinformation and knowledge within the upper tier layers of the system.The respective agents monitor for threat characteristics and patternswithin the data/information and communicate with adjunct agents that canfurther the information into various levels of situation awareness. Thesystem can be used to continuously build up and advance situationawareness and the confidence of the derived knowledge and incidentforecasts.

The system can selectively and logically maintain a repository ofdata/information determined to be of high value as well as informationthat are directly correlated with an incident. This episodic memoryallows for historical reasoning to be performed in support of automatedand human guided forensics. For example, the system end nodes canmaintain a database of scanned vehicle license plates for vehiclestraveling in proximity of high-value target areas and other selectgeo-corridors. Hence, when a vehicle becomes an investigative orcriminal interest, e.g., for being involved in a high threat sensorincident, the license plate database can be searched to determine ifthat vehicle was previously geo-located at a potential target locationor other locations of interest. The license plate search may beaccomplished in parallel at any number of selected end nodes.Additionally, information about the vehicle of interest can triggermultifaceted searches of public and secure data bases to qualify thethreat level and to identify associated threats. This information can beused in conjunction with predictive reasoning methods to narrow thesearch space and more accurately forecast and track the elementsinvolved in an incident.

The present system can be configured to apply adaptive system logicwithin several contexts. Adaptive logic is used to shift threatreasoning thresholds as a result of both local and regional events. Thisallows for the system to be more sensitive to sensor reports as neededand as a function of the overall threat “climate.” The system is alsocapable of self adaptation of distributed resources to align with moreefficient bandwidth, improved processing performance, and failed systemnodes. Adaptation of reasoning strategies and the associated parameterscan be performed which allows for flexibility and completeness ofawareness states. Further, the system can learn over time tocharacterize both steady state conditions as well as conditions that areindicative of an anomaly or threat.

The system can incorporate simulation components that are used forincident theory extrapolation and confidence reinforcement. Simulationtemplates are established a priori and are triggered when the templateparameters are fulfilled within some threshold window. The templates arethen used to direct a selected simulation component to extrapolate theknown facts and predict the outcomes at variant stages. The simulationmay also be human guided and will support the “what if” forecasts duringthe tracking of an event or potential event.

The present system can be configured as an intelligent system whichleverages artificial intelligence methodologies to present complicatedinformation in a way that humans can rapidly interpret, understand andact upon with high levels of situational awareness. As an adaptiveassociate system, the present system can support reasoning withuncertainty to build counter-terror awareness and provide preemptiveplans and coordinate routing and communications with first responders.The present system can provide an autonomous capability to preemptterrorist threats by aggregating and analyzing data, searching forpatterns indicative of unfolding terror situations, then predicting andgenerating preemptive counter-measures to terror-based situations.

Embodiments of the invention and all of the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe invention can be implemented as one or more computer programproducts, i.e., one or more modules of computer program instructionsencoded on a computer readable medium for execution by, or to controlthe operation of, data processing apparatus. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, a composition of matter effecting amachine-readable propagated signal, or a combination of one or morethem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, embodiments of the invention canbe implemented on a computer having a display device, e.g., a CRT(cathode ray tube) or LCD (liquid crystal display) monitor, fordisplaying information to the user and a keyboard and a pointing device,e.g., a mouse or a trackball, by which the user can provide input to thecomputer. Other kinds of devices can be used to provide for interactionwith a user as well; for example, feedback provided to the user can beany form of sensory feedback, e.g., visual feedback, auditory feedback,or tactile feedback; and input from the user can be received in anyform, including acoustic, speech, or tactile input.

Embodiments of the invention can be implemented in a computing systemthat includes a back end component, e.g., as a data server, or thatincludes a middleware component, e.g., an application server, or thatincludes a front end component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the invention, or any combination ofone or more such back end, middleware, or front end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of an invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or a variation of a subcombination.

Only a few implementations are disclosed. However, it is understood thatvariations and enhancements may be made.

1. A network system for collecting information from sensors, comprising: a plurality of different tier networks in communication with one another, each tier network comprising a plurality of network nodes one of which is configured as a command center of the tier network to collect data from other network nodes within the tier network and to direct the collected data to a center node of a superordinate tier network, the different tier networks configured to perform different and tier-specific data collection and data processing tasks and at least one tier network being located within a superordinate tier network; and a plurality of sensors spatially distributed in the different tier networks to perform sensing measurements, each sensor in communication with a respective network node to direct data of sensing measurements to the respective network node.
 2. The system as in claim 1, wherein: the different tier networks comprise: a plurality of subordinate tier networks at different geographic regions, respectively; a first tier network that is superordinate to the plurality of subordinate tier networks and encompasses at least a first subordinate tier network in the plurality of subordinate tier networks; and a second tier network of a same tier ranking with the first higher tier network to encompass at least a second subordinate tier network in the plurality of subordinate tier networks.
 3. The system as in claim 2, wherein: the different tier networks comprise a third tier network that is superordinate to and encompasses the first and the second tier networks, the third tier network comprising a third command center in communication with a first command center in the first tier network and a second command center in the second tier network.
 4. The system as in claim 1, wherein: the different tier networks comprise: a plurality of subordinate tier networks are differentiated from one another based on non-geographic network attributes.
 5. The system as in claim 4, wherein: one of the plurality of subordinate tier networks is a network of credit card computer servers or a network of other financial record computer database network.
 6. The system as in claim 4, wherein: one of the plurality of subordinate tier networks is a network of automatic teller machines.
 7. The system as in claim 4, wherein: one of the plurality of subordinate tier networks is a wireless mobile communication network.
 8. The system as in claim 4, wherein: one of the plurality of subordinate tier networks is an on-line service computer server network.
 9. The system as in claim 1, comprising: two different tier networks share one or more common network nodes while other network nodes in the two different tier networks are different.
 10. The system as in claim 1, comprising: a processing mechanism to distribute data processing to the plurality of different tier networks and to produce a response based on the distributed data processing.
 11. The system as in claim 10, wherein: the processing mechanism is adaptive to a history of responses of the system.
 12. The system as in claim 10, wherein: the processing mechanism comprises a neural network processing logic.
 13. The system as in claim 10, wherein: the processing mechanism comprises a fuzzy logic processing mechanism.
 14. The system as in claim 10, wherein: the sensors include sensors to detect a security threat and the processing mechanism produces a response to a confirmed security threat.
 15. The system as in claim 14, wherein: one or more tier networks in the plurality of different tier networks include network nodes at roadway intersections; the sensors include automatic license plate recognition sensors to obtain data on vehicle license plates; and the response produced by the processing mechanism includes controlling traffic controllers to alter traffic routes at one or more locations.
 16. The system as in claim 14, wherein: one or more tier networks in the plurality of different tier networks include network nodes at locations different from roadway intersections.
 17. The system as in claim 16, wherein: one or more tier networks in the plurality of different tier networks include network nodes at border checkpoints.
 18. The system as in claim 16, wherein: one or more tier networks in the plurality of different tier networks include network nodes at airport security checkpoints.
 19. The system as in claim 16, wherein: one or more tier networks in the plurality of different tier networks include network nodes at building or facility security checkpoints.
 20. The system as in claim 16, wherein: one or more tier networks in the plurality of different tier networks include network nodes at waterway security checkpoints.
 21. The system as in claim 16, wherein: one or more tier networks in the plurality of different tier networks include network nodes at railroad security checkpoints.
 22. The system as in claim 10, comprising: an artificial episodic memory mechanism in storing data in the plurality of different tier networks, and wherein the processing mechanism is configured to perform data processing based on the artificial episodic memory mechanism.
 23. The system as in claim 10, wherein: the plurality of different tier networks includes databases that store historic event scenarios, and the processing mechanism is configured to perform data processing in part based on the stored historic event scenarios.
 24. The system as in claim 10, wherein: the plurality of different tier networks includes databases that store event scenario simulation data, and the processing mechanism is configured to perform data processing in part based on the stored event scenario simulation data.
 25. The system as in claim 1, wherein: the sensors include at least one or more sensors that detect at least one of a target chemical material, a target biological material, a target radiological material, a target nuclear material, and a target explosive material.
 26. The system as in claim 1, wherein: each command center in a tier network comprises a database to store data in the tier network, a situation awareness module to provide event detection, event forecasting, assessment and response, a analytic module to analyze data characteristics, and a communication module to provide communications with adjacent subordinate and superordinate tier networks.
 27. The system as in claim 26, wherein: the communication module in each command center in a tier network comprises a communication mechanism to link the command center to one or more databases or systems outside the system to retrieve information outside the system into the system for data collection and processing.
 28. The system as in claim 1, wherein: each command center in a tier network comprises an user interface to allow a person to interact with the system.
 29. The system as in claim 1, wherein: each of the plurality of different tier networks is configured to operate independently and autonomously with respect to one or more other tier networks that are subordinate or equal in tier ranking.
 30. The system as in claim 1, wherein: a command center in each of the plurality of different tier networks is configured to directly communicate with a node in the respective tier network and in one or more other tier networks that are subordinate in tier ranking to the respective tier network.
 31. A network system for collecting information from sensors, comprising: a plurality of sensors spatially distributed in a region of interest to perform sensing measurements; a plurality of sensor communication nodes, each sensor communication node in communication with at least one of the sensors to receive data from the at least one sensor; communication links that link the sensor communication nodes into a plurality of tier networks of sensor communication nodes based on geographic location attributes and non-geographic location attributes, the plurality of tier networks being configured to perform different data collection and data processing tasks based on the tier network attributes; and a processing mechanism to distribute data processing to the plurality of tier networks and to produce a response based on the distributed data processing.
 32. The system as in claim 31, wherein: the processing mechanism is adaptive to a history of responses of the system.
 33. The system as in claim 31, wherein: the processing mechanism comprises a neural network processing logic.
 34. The system as in claim 31, wherein: the processing mechanism comprises a fuzzy logic processing mechanism.
 35. The system as in claim 31, wherein: the sensors include sensors to detect a security threat and the processing mechanism produces a response to a confirmed security threat.
 36. The system as in claim 31, comprising: an artificial episodic memory mechanism in storing data in the plurality of tier networks, and wherein the processing mechanism is configured to perform data processing based on the artificial episodic memory mechanism.
 37. The system as in claim 31, wherein: the plurality of tier networks includes databases that store historic event scenarios, and the processing mechanism is configured to perform data processing in part based on the stored historic event scenarios.
 38. The system as in claim 31, wherein: the plurality of different tier networks includes databases that store event scenario simulation data, and the processing mechanism is configured to perform data processing in part based on the stored event scenario simulation data.
 39. The system as in claim 31, wherein: the sensors include at least one or more sensors that detect at least one of a target chemical material, a target biological material, a target radiological material, a target nuclear material, and a target explosive material.
 40. The system as in claim 31, wherein: the tier networks in the plurality of tier networks are configured as in a subordinate-superordinate relationship with one another, and each tier network comprises a node as a tier command center which comprises: a database to store data in the tier network, a situation awareness module to provide event detection, event forecasting, assessment and response, a analytic module to analyze data characteristics, and a communication module to provide communications with adjacent subordinate and superordinate tier networks.
 41. The system as in claim 40, wherein: the communication module in each command center in a tier network comprises a communication mechanism to link the command center to one or more databases or systems outside the system to retrieve information outside the system into the system for data collection and processing.
 42. The system as in claim 40, wherein: each command center in a tier network comprises an user interface to allow a person to interact with the system.
 43. The system as in claim 40, wherein: each of the plurality of different tier networks is configured to operate independently and autonomously with respect to one or more other tier networks that are subordinate or equal in tier ranking.
 44. The system as in claim 40, wherein: a command center in each of the plurality of different tier networks is configured to directly communicate with a node in the respective tier network and in one or more other tier networks that are subordinate in tier ranking to the respective tier network.
 45. A method for using a security network, comprising embedding threat detection software in a plurality of roadway traffic signal controllers; coupling at least one threat detection sensor to a corresponding roadway traffic signal controller; and monitoring output of the at least one threat detection sensor by the corresponding roadway traffic signal controller.
 46. The method as in claim 45, comprising: providing sensor nodes each comprising at least one threat detection sensor at locations separate from roadway traffic signal controllers; and processing data from the roadway traffic signal controllers and the sensor nodes to determine a threat condition in the security network.
 47. A security network, comprising a plurality of second tier networks, a plurality of third tier networks; and a plurality of fourth tier networks, wherein at least one of the second tier networks comprises a plurality of fourth tier networks, and wherein each network is assigned tasks corresponding to the tier.
 48. The network as in claim 47, comprising: sensors distributed in the second, third and fourth tier networks within the security network to perform sensing measurements each indicative of a security condition at a location of a respective sensor, wherein the second, third and fourth tier networks collaborate to process data from the sensors and to perform analysis of processed data to generate a threat status in the security network. 